James starts off reminding us of the promise of personalized medicine: instead of "one size fits all," we will have "the right drug to the right patient at the right time." Instead of 100 people getting a drug, and 5 of them having a nasty allergic reaction to it, we'd get the message in advance that these particular 5 folks should not be given that drug. Personalized medicine seems to be quite effective nowadays, for example, in breast cancer treatment, where patients are tested and their tumors are found to be sensitive or insensitive to various chemotherapies before they are administered.

James goes on to discuss personalized medicine in some depth. Why the gap--why, at least so far, has personalized medicine seemed to hover just beyond reach? Theoretically, there's the problem that personalized medicine is yet one more way we've devised to believe in a false genetic determinism, when as James reminds us, "disease and health are the result of complex interactions between networks of genomic and epigenetic processes, behavior, and the environment." On a more practical level James suggests that personalized medicine depends on clouds of billions of data points for each individual, which would first be extremely expensive, and second, more importantly, would not guarantee that any resulting associations would have either a large effect size or a high predictive probability.

There's a difference, for instance, between our ability to "prove" that high blood pressure causes more cardiovascular disease, which is consistently true at the population level, and our ability to make predictions at the individual level, where many low-blood-pressure folks continue to develop heart disease and high-pressure folks remain healthy. James summarizes, "Empirical evidence across a range of medical conditions indicates that prediction based on individual genomes compares poorly in clinical value to that of clinical risk markers currently employed in conventional medicine."[1] He also noted, "When identical DNA samples were sent to leading commercial suppliers, a comparison of genomic test results found that the information supplied about individual risk profiles was not merely without clinical value but that the level of risk reported differed markedly between the companies."[2] Thus those firms that claim to be able to assist one in obtaining personalized medicine results seem incapable of this--yet quite capable of pocketing their fees.

The story so far at least seems to be that some genetic traits, which are now mostly well known (such as Huntington's disease and cystic fibrosis) have a clear and significant linkage between one's genes and one's medical prognosis, and tests are now available for many of these. By contrast, what has come forth since the mapping of the human genome is genome-wide association studies, which (for instance) have shown numerous small associations related to one's risk of developing type 2 diabetes, but even in higher-risk groups these have been mostly worthless in predicting individual risk. James adds that no group has yet been shown to have a different life span due to the cumulative effect of genome-wide associations with increased risks for coronary artery disease, type 2 diabetes, or cancer.[3]

James then turns to the two "laws." He states, "To the extent that health expenditures draw upon finite resources, development of the genomic and information technologies that underpin personalised medicine will have the unintended consequences of diverting resources away from other healthcare priorities. The inverse care law predicts that any such redirection of resources will disproportionately disadvantage those with lowest income and greatest healthcare need, who in turn have fewer resources to offset shortfalls arising from any diminution in existing healthcare."

He then addresses the inverse benefit law: "Whereas the inverse care law elucidates harm that exposure to market forces may have on the distribution of healthcare, its generalisation in the form of the inverse benefit law illustrates market-related harm of a different kind; namely, the treatment of ever more healthy populations." He gives the example (only one of many possible) of shifting treatment from osteoporosis (bone mineral density 2.5 times or more below population mean) to osteopenia or "pre-osteoporosis" (BMD as little as 1.0 SD below mean). This leads predictably to two outcomes--first, treating many more healthy people with drugs that will do them no good, and second, exposing many more of them to the risks of adverse drug reactions. James notes this amounts to drug treatment for those "at risk of being at risk."[4]

James summarizes, "the extreme levels of human and material resources required by the genomic and information technologies upon which personalised medicine is based threaten to disproportionately disadvantage those with greatest healthcare needs (inverse care law)... while simultaneously posing harm to ever larger populations of health individuals (inverse benefits law)....A mix of excessive confidence in personalised medicine, high expectations of benefits, perceived commercial opportunities, and insufficient attention to harmful consequences has the potential to 'colonise the future' of healthcare...wherein attention and resources are captured at the expense of alternative behavioural and social pathways that have the potential to effect greater improvements in population health."